Marketing expenses are often one of the largest cost categories for an organization. Marketing difficulties in effectively capturing and reaching the correct population of consumers is an industry wide problem, regardless of goods or services offered. In an attempt to overcome these difficulties, entities often engage in various advertising techniques to a broad audience hoping to reach interested consumers. However, such broad advertising techniques are often ignored by consumers or fail to reach the intended audience.
Using relevant data, population characteristics typically provide an effective form of targeted marketing by creating a shopping experience that is personalized and relevant to the consumer. However, targeted marketing systems are often limited to accessing a unique set of data that provide a holistic view of a consumer's spending habits and preferences. For instance, online retailer Amazon may have information regarding the products purchased by a particular consumer on their e-commerce site, but they lack the information on the type of products and services the same consumer purchases from other merchants.
However, generating population characteristics is often based on a subset of the population's responses to surveys, such as the U.S. census. This often leads to inaccurate results due to subjective categories, poor correlation of data, and responses based on a respondent's biased self image. Also, survey participation is time consuming and avoided by large subsets of the population. Such deficiencies often lead to gaps in the data.
Therefore, a long-felt need exists for a method to leverage the large amount of data available to some financial processors to provide an enhanced population segmentation and characteristics system.